Benjamin Buysschaert

Sustained accumulation of prelamin A and depletion of lamin A/C both cause oxidative stress and mitochondrial dysfunction but induce different cell fates

The cell nucleus is structurally and functionally organized by lamins, intermediate filament proteins that form the nuclear lamina. Point mutations in genes that encode a specific subset of lamins, the A-type lamins, cause a spectrum of diseases termed laminopathies. Recent evidence points to a role for A-type lamins in intracellular redox homeostasis. To determine whether lamin A/C depletion and prelamin A accumulation differentially induce oxidative stress, we have performed a quantitative microscopy-based analysis of reactive oxygen species (ROS) levels and mitochondrial membrane potential (Δψm) in human fibroblasts subjected to sustained siRNA-mediated knockdown of LMNA and ZMPSTE24, respectively. We measured a highly significant increase in basal ROS levels and an even more prominent rise of induced ROS levels in lamin A/C depleted cells, eventually resulting in Δψm hyperpolarization and apoptosis. Depletion of ZMPSTE24 on the other hand, triggered a senescence pathway that was associated with moderately increased ROS levels and a transient Δψm depolarization. Both knockdowns were accompanied by an upregulation of several ROS detoxifying enzymes. Taken together, our data suggest that both persistent prelamin A accumulation and lamin A/C depletion elevate ROS levels, but to a different extent and with different effects on cell fate. This may contribute to the variety of disease phenotypes witnessed in laminopathies.

Reevaluating multicolor flow cytometry to assess microbial viability

Flow cytometry is a rapid and quantitative method to determine bacterial viability. Although different stains can be used to establish viability, staining protocols are inconsistent and lack a general optimization approach. Very few “true” multicolor protocols, where dyes are combined in one sample, have been developed for microbiological applications. In this mini-review, the discrepancy between protocols for cell-permeant nucleic acid and functional stains are discussed as well as their use as viability dyes. Furthermore, optimization of staining protocols for a specific setup are described. Original data using the red-excitable SYTO dyes SYTO 59 to 64 and SYTO 17, combined with functional stains, for double and triple staining applications is also included. As each dye and dye combination behaves differently within a certain combination of medium matrix, microorganism, and instrument, protocols need to be tuned to obtain reproducible results. Therefore, single, double, and triple stains are reviewed, including the different parameters that influence staining such as stain kinetics, optimal stain concentration, and the effect of the chelator EDTA as membrane permeabilizer. In the last section, we highlight the need to investigate the stability of multicolor assays to ensure correct results as multiwell autoloaders are now commonly used.

Artificial sweat composition to grow and sustain a mixed human axillary microbiome

A novel artificial sweat composition, Skin Community Interaction simulation, designed to mimic the human axillary sweat, was compared to other artificial sweat compositions. Axillary microbiota grown in the novel composition closely resembled the original community. Volatile organic compound analysis showed good correlations with in vivo axillary (mal)odor components.

Flow cytometric fingerprinting for microbial strain discrimination and physiological characterization

The analysis of microbial populations is fundamental, not only for developing a deeper understanding of microbial communities but also for their engineering in biotechnological applications. Many methods have been developed to study their characteristics and over the last few decades, molecular analysis tools, such as DNA sequencing, have been used with considerable success to identify the composition of microbial populations. Recently, flow cytometric fingerprinting is emerging as a promising and powerful method to analyze bacterial populations. So far, these methods have primarily been used to observe shifts in the composition of microbial communities of natural samples. In this article, we apply a flow cytometric fingerprinting method to discriminate among 29 Lactobacillus strains. Our results indicate that it is possible to discriminate among 27 Lactobacillus strains by staining with SYBR green I and that the discriminatory power can be increased by combined SYBR green I and propidium iodide staining. Furthermore, we illustrate the impact of physiological changes on the fingerprinting method by demonstrating how flow cytometric fingerprinting is able to discriminate the different growth phases of a microbial culture. The sensitivity of the method is assessed by its ability to detect changes in the relative abundance of a mix of polystyrene beads down to 1.2%. When a mix of bacteria was used, the sensitivity was as between 1.2% and 5%. The presented data demonstrate that flow cytometric fingerprinting is a sensitive and reproducible technique with the potential to be applied as a method for the dereplication of bacterial isolates.

Laboratory-scale simulation and real-time tracking of a microbial contamination event and subsequent shock-chlorination in drinking water

Rapid contamination of drinking water in distribution and storage systems can occur due to pressure drop, backflow, cross-connections, accidents, and bio-terrorism. Small volumes of a concentrated contaminant (e.g., wastewater) can contaminate large volumes of water in a very short time with potentially severe negative health impacts. The technical limitations of conventional, cultivation-based microbial detection methods neither allow for timely detection of such contaminations, nor for the real-time monitoring of subsequent emergency remediation measures (e.g., shock-chlorination). Here we applied a newly developed continuous, ultra high-frequency flow cytometry approach to track a rapid pollution event and subsequent disinfection of drinking water in an 80-min laboratory scale simulation. We quantified total (TCC) and intact (ICC) cell concentrations as well as flow cytometric fingerprints in parallel in real-time with two different staining methods. The ingress of wastewater was detectable almost immediately (i.e., after 0.6% volume change), significantly changing TCC, ICC, and the flow cytometric fingerprint. Shock chlorination was rapid and detected in real time, causing membrane damage in the vast majority of bacteria (i.e., drop of ICC from more than 380 cells μl-1 to less than 30 cells μl-1 within 4 min). Both of these effects as well as the final wash-in of fresh tap water followed calculated predictions well. Detailed and highly quantitative tracking of microbial dynamics at very short time scales and for different characteristics (e.g., concentration, membrane integrity) is feasible. This opens up multiple possibilities for targeted investigation of a myriad of bacterial short-term dynamics (e.g., disinfection, growth, detachment, operational changes) both in laboratory-scale research and full-scale system investigations in practice.

Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data

Detecting disturbances in microbial communities is an important aspect of managing natural and engineered microbial communities. Here, we implemented a custom-built continuous staining device in combination with real-time flow cytometry (RT-FCM) data acquisition, which, combined with advanced FCM fingerprinting methods, presents a powerful new approach to track and quantify disturbances in aquatic microbial communities. Through this new approach we were able to resolve various natural community and single-species microbial contaminations in a flow-through drinking water reactor. Next to conventional FCM metrics, we applied metrics from a recently developed fingerprinting technique in order to gain additional insight into the microbial dynamics during these contamination events. Importantly, we found that multiple community FCM metrics based on different statistical approaches were required to fully characterize all contaminations. Furthermore we found that for accurate cell concentration measurements and accurate inference from the FCM metrics (coefficient of variation ≤ 5%), at least 1000 cells should be measured, which makes the achievable temporal resolution a function of the prevalent bacterial concentration in the system-of-interest. The integrated RT-FCM acquisition and analysis approach presented herein provides a considerable improvement in the temporal resolution by which microbial disturbances can be observed and simultaneously provides a multi-faceted toolset to characterize such disturbances.

Online flow cytometric monitoring of microbial water quality in a full-scale water treatment plant

The ever-increasing need for high-quality drinking and process waters, and growing public awareness about possible contamination, drive efforts for the further development of automated control of water treatment plants. For example, membrane filtration processes and reverse osmosis in particular are generally regarded as a safe barrier for inorganic, organic, and microbial contamination. Yet, to ensure the final water quality and to increase the confidence of the end-user, intensive and preferably online monitoring should be further implemented as an early-warning tool to control membrane integrity and to prevent microbial regrowth in the distributing network. In this paper, we test the applicability of flow cytometry and cytometric fingerprinting for a full-scale water treatment plant. We demonstrate in a full-scale water treatment plant that flow cytometry can be used as online monitoring tool and that changes in water quality can be observed, which are not monitored by commonly used online quality parameters. Furthermore, we illustrate with ultrafiltration that process conditions impact the flow cytometric cell counts.Bacterial monitoring: Let it flowFlow cytometry can be used as an online, automated tool to monitor water quality in full-scale water treatment plants. The presence of bacteria in high concentrations in water leads to the unwanted growth of biofilms, which can damage water treatment equipment through microbiologically influenced corrosion. Biofilms can also host pathogenic bacteria, leading to water contamination upon their shedding. Monitoring microbial communities in water treatment plants is thus crucial, and a team led by Bart De Gusseme at Ghent University in Belgium demonstrate that online flow cytometry is capable of achieving this in high throughput and at single-cell resolution. Monitoring at different stages of the treatment plant, their method provides important insights into microbiological dynamics that are unattainable through commonly used online quality parameters, and additionally finds that bacterial concentrations are directly related to process operations.

Coculturing bacteria leads to reduced phenotypic heterogeneities

Isogenic bacterial populations are known to exhibit phenotypic heterogeneity at the single cell level. Because of difficulties in assessing the phenotypic heterogeneity of a single taxon in a mixed community, the importance of this deeper level of organisation remains relatively unknown for natural communities. In this study, we have used membrane-based microcosms that allow the probing of the phenotypic heterogeneity of a single taxon while interacting with a synthetic or natural community. Individual taxa were studied under axenic conditions, as members of a coculture with physical separation, and as a mixed culture. Phenotypic heterogeneity was assessed through both flow cytometry and Raman spectroscopy. Using this setup, we investigated the effect of microbial interactions on the individual phenotypic heterogeneities of two interacting drinking water isolates. We have demonstrated that interactions between these bacteria lead to an adjustment of their individual phenotypic diversities, and that this adjustment is conditional on the bacterial taxon. Importance Laboratory studies have shown the impact of phenotypic heterogeneity on the survival and functionality of isogenic populations. As phenotypic heterogeneity is known to play an important role in pathogenicity and virulence, antibiotics resistance, biotechnological applications and ecosystem properties, it is crucial to understand its influencing factors. An unanswered question is whether bacteria in mixed communities influence the phenotypic heterogeneity of their community partners. We found that coculturing bacteria leads to a reduction in their individual phenotypic heterogeneities, which led us to the hypothesis that the individual phenotypic diversity of a taxon is dependent on the community composition.

Label-free Raman characterization of bacteria calls for standardized procedures

Raman spectroscopy has gained relevance in single-cell microbiology for its ability to detect bacterial (sub)populations in a non-destructive and label-free way. However, the Raman spectrum of a bacterium can be heavily affected by abiotic factors, which may influence the interpretation of experimental results. Additionally, there is no publicly available standard for the annotation of metadata describing sample preparation and acquisition of Raman spectra. This article explores the importance of sample manipulations when measuring bacterial subpopulations using Raman spectroscopy. Based on the results of this study and previous findings in literature we propose a Raman metadata standard that incorporates the minimum information that is required to be reported in order to correctly interpret data from Raman spectroscopy experiments. Its aim is twofold: 1) mitigate technical noise due to sample preparation and manipulation and 2) improve reproducibility in Raman spectroscopy experiments studying microbial communities.